RMRS Forest Inventory and AnalysisUser Group Meeting

2010 April 13

Why is tree canopy cover important?Percent tree canopy cover is an integral part

of both international forest land definitions and U.S. definitions of forest.

Percent tree canopy cover is an important variable both within forest land areas and in areas not traditionally considered forest.

Knowing where trees are is an important first step in quantifying carbon and managing tree resources.

What is NLCD?National Land Cover Database (NLCD)

NLCD 1992: Originated from Multi-Resolution Land Characteristics

(MRLC) consortium, a multi-agency program formed to acquire Landsat data across the conterminous U.S. and generate 30m-pixel land cover map.

NLCD 2001: 2nd generation land cover map. In addition, 30m- pixel maps of imperviousness and % tree canopy.

NLCD 2011: 3rd generation land cover, imperviousness, and %tree canopy maps.

NLCD 2001 Percent Tree Canopy Cover

From FY07 through FY09 the canopy cover layer was downloaded from MRLC 400 times per month on average (not including ftp downloads).

Input for Landfire modeling.Potential source for updating the 2000

assessment of urban tree cover as part of the Resource Planning Act Assessment.

Definition of Tree Canopy:A layer or multiple layers of branches and foliage at the top or crown of a forest’s tree.

Example of the NLCD 2001 Percent Tree Canopy Layer

2001 General Modeling Approach

=Response developed by an automated classification of tree crown cover on 1-m DOQs (with extensive post-processing hand editing).

Model developed using regression tree (random forest) algorithm

Fig. from Homer et al. 2007

2001 NLCD Zone Map

Preliminary Accuracy AssessmentsNLCD 2001 % Tree Canopy Cover

Homer et al. 2004 reported mean absolute error rates of 9.9%, 14.1%, and 8.4% in zones 16, 41, and 60 respectively.

Greenfield et al. 2009 reported under estimation of percent tree canopy cover.

An external review of the Landfire canopy cover data suggests that pixel level values are too high (http://www.landfire.gov/notifications16.php).

Rationale for using FIA dataFIA is a fundamental component of Forest Service

research. FIA is a data rich program:

% tree canopy cover estimates will be made on all FIA plots (~340,000 in conterminous US).. including sampling locations that are not consider forest land (e.g. urban, agricultural).

Consistency between map based and plot based estimates:Adopting the FIA survey design and using FIA data for developing

% tree canopy models will likely provide more consistent estimates of percent canopy cover.

The FIA survey design is easily intensified:FIA will use NAIP photography to estimate percent tree canopy on

all non-visited plots as well as a portion of visited plots.The photo interpretation techniques can be used on an intensified

FIA sample.

Rationale for using photo interpretationNational Agriculture Imagery Program (NAIP) 1-m resolution, ortho-rectified, natural color, or near

Infrared imagery for the conterminous U.S. .. and free.

Can use directly in models, with no post-processing editing.

Can test the robustness of the models using different data intensities.

Can test other photo-based responses using tree/no tree classifications, such as: classification trees or segmentation.

Can leverage information that will be collected as part of the FIA program.

General Approach: Proof of Concept In general, the proof of concept research will

rely on many of the same explanatory variables used in the 2001 product (e.g. leaf-on and leaf-off Landsat imagery, DEM derivatives).

The response variable will be developed from manual photo interpretation, modeled NAIP Imagery, and FIA plot modeling (e.g. Toney et al. 2009).

The type of model will be determined by examining various quality assessments.

There are 5 proof of concept study areas.

2011 General Modelling Approach

=Response developed by photo interpreting tree crown cover on NAIP Imagery, modeled NAIP imagery, and FIA plot modeling

Random forest algorithmK nearest neighbor imputationSupport vector machines . . .

Example modellingtechniques

Fig. from Homer et al. 2007

Key Research QuestionsAlternative pixel-level modeling techniques.Alternative stratification/grouping strategies (i.e.

alternatives to mapping zones).Using ordinal data for developing model. Model stability under different sampling

intensification levels.Impact of scale of response on tree canopy cover

estimates (30m vs 90m). Relationship among plot-based, PI-based, and

modeled estimates (Toney et al. 2009).

Proof of Concept Study Areas

Study Area Characteristics

1. Approximately one Landsat scene in area

2. Cover multiple scenes

3. Cover multiple gradientsI. urban areaII. different

vegetation types

FIA Grid and Photo IntensificationStandard IntensityFIA grid (6000 acres):

•Modeled % canopy cover estimates•Photo interpreted estimates

Intensified 4x grid (1500 acres):

•Photo interpreted % canopy cover

105 photo points per sampling location within a 90m2 area

Modeled canopy cover from field databased on stem mapping and crown diameter models from Coulston et al. 2010

Photo Sample

Repeatability Component

Between 2 and 5 interpreters are working in each study area.

5% of the sample locations are interpreted by all interpreters

Comparison will be conducted among interpreters, and by different landscapes (e.g. urban, heavily forested, agricultural).

Compare and Contrast: 2001 to 2011 Approaches

Response data developed from data with similar grain size (1m)

2001 product developed from fewer (3-4) large (1-4 km2) sample locations. 2011 proof of concept approach relies on many (~4100) small (0.81 ha) sample locations.

Sample locations were purposive for 2001. Sample locations drawn randomly 2011.

Explanatory variables are similar.Modelling approaches will be similar although additional

approaches will be examined as part of the 2011 proof of concept.

Cost is likely higher using 2011 approach but some of the work is part of the core FIA program.

StatusProof of concept work funded

Photo interpretation 90% completedExplanatory data stacks in placeMain research components to be completed

FY10Internal Forest Service funding proposal to

be submitted March 2010Prototyping work 2011 (dependent upon

additional funding)Production work 2012-2013 (dependent upon

additional funding)

Team Members1 Ken Brewer, Quantitative Sciences Staff WO1,2 Warren Cohen, Pacific Northwest Research Station1,2 John Coulston, Southern Research Station1,2 Mark Finco, Remote Sensing Applications Center1 Everett Hinkley, Remote Sensing National

Coordinator1,2 Gretchen Moisen, Rocky Mountain Research Station1 Frank Sapio, Forest Health Technology Enterprise

Team1 Brian Schwind, Remote Sensing Applications Center1,2 Ty Wilson, Northern Research Station

1Leadership Team2Science Team

Questions? 4x Intensity Photo-basedSample Locations

105 photo points to estimate% tree canopy cover for modeldevelopment

E.G. NLCD 2001 % Tree Canopy cover

E.G. NLCD 2001 % Tree Canopy cover

References Homer, C., Haung, C., Yang, L., Wylie, B., Coan, M. 2004. Development

of a 2001 national land-cover database for the United States. Photogrammetric Engineering and Remote Sensing 70, 829-840.

Greenfield E.J., Nowak D.J., Walton J.T. 2009. Assessment of 2001 NLCD Percent Tree and Impervious Cover Estimates. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING. 75(11). 1279-1286.

Toney, C., Shaw, J.D., Nelson, M.D. 2009. A stem-map model for predicting tree canopy cover of forest inventory and analysis (FIA) plots. In: McWilliams, W., Moisen, G., Czaplewski, R. comps. 2009. 2008 Forest Inventory and Analysis (FIA) symposium. October 21-23, 2008. Park City, UT. Proc. RMRS-P-56CD. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 1 CD.

Coulston, J.W., Oswalt, S.N., Carraway, A.B., Smith, W.B. 2010. Assessing forest land area based on canopy cover in a semi-arid region: a case study. Forestry, doi:10.1093/forestry/cpp039